Conventionally, tools have existed to search text-based databases for specified words, word fragments, phrases, etc. More recently, effort has been devoted to achieving similar results with respect to collections of electronic images. Thus, for example, one might like to conduct an automated search of an archive of millions of digital photographs in order to find all of the photographs that include an image of a person's face.
One known approach to object detection uses a prediction technique to generate multiple feature classifiers from a set of training image frames and then uses those classifiers for detecting faces. An example of a training approach 5 for generating the feature classifiers is illustrated in FIG. 1. Training samples 10, consisting of a number of image frames, initially are input into a segmentation stage 12. In segmentation stage 12, various individual windows are identified for each image frame, each such window being a sub-portion of the subject image frame. Each output window is normalized 14 and then subject to training 16 to obtain, based on an available set of features (or predictors) 17, multiple classifiers 18. Each such classifier 18 generally is based on multiple features selected from set 17.
An object detector 30 that subsequently is constructed using the classifiers 18 generated in accordance with training approach 5 is illustrated in FIG. 2. Input image frames 32 consist of a number of individual image frames in which one would like to identify any images of people's faces. Each input image frame 32 is segmented 12 and then normalized 14 in the same manner as was used for training data 10. Finally, using the identified classifiers 18, a determination is made in classification stage 34 as to whether or not each normalized window contains an image of a person's face.
While the foregoing technique provides fairly accurate results, additional improvements are desirable.